Artificial Intelligence-Guided Pulsed Synthesis of Zinc Oxide Nanostructures on Thin Metal Shells

Abstract

Zinc oxide (ZnO) nanostructures have been intensively investigated for applications in sensing, photocatalysis, and optoelectronic devices, where functional performance is strongly governed by morphology, crystallinity, and defect structure. Conventional wet-chemical and vapor-phase growth methods often require long processing times or complex chemistries and face reproducibility and compatibility challenges when applied to thin, flexible, or curved metallic substrates. Pulsed high-energy techniques—such as pulsed laser deposition (PLD), high-power impulse magnetron sputtering (HiPIMS), and pulsed laser or plasma processing—offer a versatile alternative, enabling rapid and localized synthesis both from and on Zn-bearing thin shells. These methods create transient nonequilibrium conditions that accelerate oxidation and promote spatially controlled nanostructure formation. This review highlights the emerging integration of artificial intelligence (AI) with pulsed ZnO synthesis on thin metallic substrates, emphasizing standardized data reporting, Bayesian optimization and active learning for efficient parameter exploration, physics-informed and graph-based neural networks for predictive modeling, and reinforcement learning for adaptive process control. By connecting synthesis dynamics with data-driven modeling, the review outlines a path toward predictive and autonomous control of ZnO nanostructure formation. Future perspectives include autonomous experimental workflows, machine-vision-assisted diagnostics, and the extension of AI-guided pulsed synthesis strategies to other functional metal oxide systems.

1. Introduction

Zinc oxide (ZnO) is a wide-bandgap semiconductor whose one-dimensional (nanowires, nanorods) and two-dimensional (nanosheets, ultrathin films) nanostructures have been extensively investigated for applications in sensing, optoelectronics, photocatalysis, and energy conversion. The functional performance of ZnO is highly sensitive to morphology, crystallinity, and defect chemistry; thus, synthesis strategies that enable reproducible control over geometry, aspect ratio, and defect populations are critical for translating laboratory prototypes into practical devices [1]. Conventional approaches—including hydrothermal growth, chemical bath deposition, vapor-phase transport, chemical vapor deposition, and established thin-film methods—can yield high-quality ZnO nanostructures. However, they typically involve long processing times, catalysts, or complex precursors, and face limitations in reproducibility, substrate compatibility, and morphological versatility, particularly for thin, flexible, or curved metallic supports [2].

Among various synthesis methods, pulsed high-energy techniques—including pulsed laser ablation in liquids [3,4], plasma-assisted synthesis [5], high-power impulse magnetron sputtering (HiPIMS) [6] and pulsed thermal oxidation [7]—offer a versatile and rapid pathway for forming ZnO nanostructures directly on thin metallic substrates. Pulsed energy input can generate diverse transient nonequilibrium conditions—such as local thermal gradients, stress waves, and rapid energy localization—that accelerate oxidation kinetics and enable spatially selective self-organization [8]. For thin metallic shells (films, plated foils, curved thin substrates), the mechanical response—eigenmodes, localized vibration, and stress concentration—becomes an additional, tunable degree of freedom that can steer where and how Zn migrates and oxidizes [9]; similar effects were reported for brass foils under pulsed laser irradiation, leading to the formation of quasi-one-dimensional ZnO nanowire structures [10]. The morphology and structural diversity of ZnO nanomaterials critically influence their functional performance in diverse applications [11,12].

The synthesis of ZnO under pulsed energy input is inherently multiparametric and multiphysical, governed by pulse fluence, duration, repetition rate, and spatial energy distribution, which are coupled to substrate mechanics and stress-assisted mass transport. The high dimensionality and nonlinear interdependence of these factors limit reproducibility and hinder predictive control. To overcome these challenges, artificial intelligence (AI) methods have emerged as efficient tools for modeling, optimization, and adaptive process guidance [13]. Bayesian optimization supports efficient exploration of multidimensional parameter spaces [14]; physics-informed neural networks (PINNs) incorporate governing equations into predictive frameworks [15]; graph neural networks (GNNs) capture structural correlations in hierarchical systems [16]; and reinforcement learning (RL) enables real-time, feedback-driven synthesis optimization [17]. Nevertheless, the limited development of standardized experimental datasets and metadata continues to constrain progress [18]. Implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles [19] and shared data infrastructures [20] is increasingly recognized as essential to ensure reproducibility, interoperability, and transparency in materials research.

In concert with mechanical and thermal models that describe the coupling of localized energy absorption, stress evolution, and oxidation-driven pattern formation, AI-assisted approaches provide a physically grounded, data-driven route to reproducible control of ZnO nanostructure morphology. This review summarizes current advances in the pulsed synthesis of ZnO nanostructures on thin metallic substrates, emphasizing the integration of AI and physical modeling as a foundation for adaptive, scalable, and rational design of functional ZnO-based systems.

2. Technological Implementation of Pulsed Synthesis of ZnO on Thin Metallic Substrates

2.1. Pulsed Synthesis and Morphology Control of ZnO on Thin Metallic Substrates

Pulsed energy techniques—including pulsed laser deposition (PLD), pulsed laser irradiation or annealing, pulsed plasma treatments, and fast electrical or thermal pulses—offer a fundamentally distinct approach to forming ZnO on thin metallic substrates compared with equilibrium synthesis methods [21]. Because the energy is delivered in short bursts—often shorter than the characteristic relaxation times of thin metallic substrates—the metal layer experiences ultrafast non-equilibrium excitations ranging from localized heating and quenching to plasma-assisted surface chemistry. These transient conditions initiate rapid phase transformations and localized reactions instead of slow lattice diffusion or equilibrium processes [22].

Thin metallic substrates serve multiple roles in pulsed processes: they can act as atom reservoirs supplying metal species during high-energy pulses, as thermal and electrical mediators redistributing deposited energy, and as mechanical frameworks defining interfacial stress and adhesion. Their composition and architecture further influence nucleation pathways, preferred orientations, and defect formation [23]. Together, pulsed energy deposition and the multilayer structure of the substrate promote the self-organization of highly textured ZnO morphologies—such as vertically aligned nanorods, nanowires, nanosheets, and nanowalls—distinct from the continuous polycrystalline oxide layers typically obtained by equilibrium synthesis [24].

The resulting ZnO morphology depends on (i) pulse duration, peak fluence, and repetition rate; (ii) ambient atmosphere (oxygen partial pressure, reactive species, and gas background); (iii) the properties of thin metallic shells, such as thickness, conductivity, wetting, and roughness; and (iv) the underlying substrate, which acts as a thermal sink and defines lattice constraints [25,26]. As shown in [26], the PLD microstructure of ZnO films is strongly affected by deposition conditions—grain size, continuity, and columnar growth depend on oxygen pressure, temperature, and repetition rate. Variations in substrate temperature (350–700 °C) and ambient conditions (vacuum or 0.7 mbar O₂) lead to significant changes in grain morphology, porosity, and electrical resistance, as illustrated in Figure 1.

In PLD, energetic species in the ablation plume condense and nucleate on the substrate. By tuning the background pressure and pulse energy, columnar c-axis–oriented ZnO can form even at relatively low substrate temperatures, unlike vapor–liquid–solid or hydrothermal growth [27]. Focused pulsed irradiation of pre-deposited Zn or Zn-containing layers, using ultraviolet to visible lasers with pulse durations ranging from femtoseconds to nanoseconds, enables localized ZnO formation via melting, oxidation, and resolidification, allowing for direct “writing” of photoluminescent and conductive features without global heating [28]. Pulse duration affects nucleation density and feature scale: picosecond and femtosecond pulses generate finer features, whereas nanosecond pulses produce larger molten zones. Adjusting repetition rate and scanning strategy can induce annealing or layer-by-layer growth, offering precise morphological control [29].

The strong non-equilibrium nature of pulsed processing yields a high density of point defects in ZnO—especially oxygen vacancies (Vₒ)—and complex clusters that modify optical and electronic properties. These defects increase oxygen vacancy concentrations, which in turn leads to stronger deep-level emission (typically observed as green luminescence), thereby enhancing gas-sensing performance and photocatalytic activity [30]. However, excessive defect density can degrade mobility and stability; thus, pulse parameters and post-anneals must be tuned for each application. The rapid quenching can also stabilize metastable phases and strain fields unattainable by slow annealing [31].

The thin metal shell governs atom supply and heat flow. Under intense pulses, Zn can partially vaporize and reoxidize into ZnO during cooling. When Zn overlies other metals or conductive oxides, interfacial reactions and templating effects guide nucleation and orientation. The high conductivity of the metal underlayer confines heating to the surface, enabling ultrafast cooling and arresting diffusion, which produces oriented nanostructures with high defect concentration and strong adhesion [32,33]. The shell material and thickness determine how pulses couple into the structure and thus influence both morphology and defect chemistry [34].

These ZnO–metal systems synthesized using pulsed techniques exhibit enhanced gas sensing, photoluminescence, and photocatalytic activity due to high surface area and controlled defect states [26,35,36]. Vertically aligned nanowires offer strong light scattering for photonic and optoelectronic devices [37]. Challenges remain in achieving wafer-scale uniformity, minimizing defects for electronic-grade films, and preventing substrate damage. Integrating in situ ultrafast diagnostics, plasma spectroscopy, and multiscale modeling will advance process control [38].

In conclusion, pulsed energy routes enable oriented, defect-engineered ZnO thin films and coatings with tunable morphology and composition. By optimizing pulse parameters, ambient chemistry, and metal-shell structure, ZnO orientation and defect populations can be tailored for sensing, photonic, and optoelectronic uses. Future priorities include scale-up, reproducibility, and mechanistic modeling for device integration.

2.2. Overview of Pulsed Synthesis Techniques

Diverse pulsed techniques have been developed for the fabrication of ZnO nanostructures on thin metallic substrates. Femtosecond laser processing enables the creation of nanoscale/submicrometer structures (waveguides, gratings, nanopillars) through nonthermal ablation [39]. PLA provides localized energy input, promoting rapid ZnO formation via target evaporation for subsequent condensation. Pulsed laser deposition (PLD), based on laser ablation, allows for the controlled growth of oriented ZnO layers suitable, for example, for optoelectronic integration [40,41]. PLD can be applied not only for the controlled growth of oriented thin ZnO films, but also for the fabrication of one-dimensional (1D) nanostructures, offering precise control over their morphology and orientation. By using sacrificial interlayers, morphology transitions from films to vertically aligned nanorods have been achieved, enhancing photodetector performance [42]. Figure 2 illustrates the PLD growth mechanism of ZnO(002) nanorods from a ZnS seed layer: during heating, the ZnS film decomposes, forming Zn islands that act as templates for the epitaxial growth of ZnO nanorods with single-crystal hexagonal (002) orientation [42].

Seed-assisted PLD yields vertically aligned nanowires [43], and high-temperature PLD produces columnar morphologies with tunable optical properties [44]. These examples illustrate the versatility of pulsed routes for directing ZnO into functional 1D geometries, highly relevant for sensing, photonics, and flexible device applications.

Using pulsed laser oxidation of Cu–Zn alloys, ZnO nanostructures can be directly formed through thermoacoustic effects, enabling in situ patterning without a seed oxide [45]. Figure 3 illustrates the resulting morphologies of ZnO nanostructures on Cu–Zn alloys: disordered structures form in peripheral zones, arrays of nanowires appear toward the center, and nanosheets as well as hybrid two-dimensional morphologies develop in the central region. These morphology transitions are governed by local temperature and zinc concentration during pulsed laser oxidation. These transformations arise from non-stationary thermal and acoustic effects that accelerate zinc oxidation and its diffusion to the surface.

Plasma-discharge and spark-based methods—including atmospheric plasmas, micro-arc treatments, and spark discharges—generate ZnO thin films under strongly non-equilibrium conditions, favoring defect-rich or metastable structures. Minimal thermal load makes these methods compatible with flexible or thermally sensitive substrates. In liquids, spark discharges produce ZnO nanocolloids with tunable size distributions, extending pulsed plasma synthesis from films to nanoparticle suspensions [46].

Hybrid pulsed approaches further extend morphology and defect control by coupling electrical discharges with electrochemical environments. For example, pulsed electrochemical discharges in electrolytes enable the formation of hexagonal ZnO nanorods, where higher voltage and duty cycles accelerate growth and increase particle size [47]. Micro-arc oxidation (MAO) under pulsed high-voltage discharges at the metal–electrolyte interface produces porous oxide coatings, including zinc-based composites, with improved adhesion, biocompatibility, and corrosion resistance [48]. These hybrid schemes demonstrate the versatility of pulsed approaches on curved or flexible substrates.

Overall, pulsed techniques offer complementary advantages in scalability, thermal compatibility, and morphology control. Laser-based approaches (PLA/PLD and femtosecond writing) allow for high-precision stoichiometry and patterning, HiPIMS combines high film quality with industrial relevance [49], while plasma- and spark-based strategies provide low-temperature processing compatible with flexible substrates. Other schemes, such as laser-induced chemical deposition [22] and atmospheric-pressure plasma deposition [50], further broaden the toolbox for ZnO nanostructure fabrication.

2.3. Pulse Parameters and Energy Sources

In pulsed synthesis of ZnO nanostructures on thin metal shells, mechanisms differ between laser irradiation, plasma discharges, and hybrid systems, but a common principle applies: pulse characteristics govern energy transfer and reaction kinetics at the substrate interface. Key parameters—amplitude, duration, repetition rate, and temporal profile—control energy deposition, electron–lattice interactions, and ZnO nucleation and growth, allowing precise tuning of morphology, defect density, and crystallinity [51].

In PLD and related ablation schemes, amplitude is defined by fluence (J/cm²). Adjusting fluence allows for transitions between evaporation, plasma formation, or fragmentation regimes, affecting ZnO microstructure, particle size, and phase composition [52,53]. In HiPIMS, peak current and voltage determine plasma density and ion energy distribution, thereby controlling the flux and energy of charged species arriving at the substrate [54]. In pulsed ablation within liquids, pulse energy governs cavitation dynamics and local chemistry, influencing ZnO nucleation and growth; solvent properties, including coordination ability and chemical character, modulate growth kinetics and particle morphology [55,56].

Pulse duration is a critical parameter. For example, in localized laser processing, such as patterning or direct writing, pulse duration defines the balance between thermal and nonthermal processes. Nanosecond–millisecond pulses generally produce thermal effects such as melting or stress accumulation. Millisecond lasers are rarely used, since their slow energy delivery causes overheating and remelting rather than controlled growth. Nevertheless, periodic millisecond irradiation of Cu–Zn alloys can selectively oxidize Zn into ZnO nanowires via combined thermal and acoustic effects [57]. Nanosecond pulses are widely employed for laser ablation or annealing [58], whereas picosecond to femtosecond pulses enable nonthermal regimes that minimize substrate heating and produce ZnO with superior crystallinity and interfacial stability [59]. These ultrafast modes are particularly important for thin foils and flexible substrates, where excessive heating may result in deformation or delamination.

In other approaches, ultrashort pulses in the picosecond–femtosecond regime are generally not employed due to limitations in power electronics and discharge physics. HiPIMS and pulsed direct-current sputtering typically use microsecond pulses, whereas pulsed radio-frequency or microwave plasmas operate in the micro- to millisecond range to stabilize deposition and minimize substrate heating [60]. The repetition rate and duty cycle determine the cumulative thermal load: low frequencies favor discrete nucleation, while higher rates enhance diffusion and structural reorganization [61]. In HiPIMS, these parameters also control film density and morphology, enabling transitions from columnar to compact growth [62].

Reactive HiPIMS and other pulsed plasma techniques allow for the tuning of structural, morphological, and optical properties of ZnO nanomaterials by controlling plasma composition and operating conditions [63]. Hybrid approaches that combine PLD and radio-frequency magnetron sputtering (RFMS) or other pulsed methods provide additional control over ionization pathways, defect states, and growth dynamics, enabling ZnO coatings with tailored optoelectronic functionality [64,65]. By adjusting deposition parameters, the morphology of ZnO nanostructures can be guided from vertically aligned nanorods to more complex or hybrid forms, as illustrated in Figure 4.

Table 1. Comparative summary of pulsed ZnO synthesis techniques.

Method	Operating Parameters	Substrate Temperature *	Growth Rate/Throughput	Morphology Control	Defect Characteristics	References
PLD	Fluence 1–4 J/cm2, p(O2) 10−5–10−1 mbar, 1–10 Hz	RT–700	2–9 nm/min	Nanocolumns, c-axis films, thin films, nanorods	Vₒ tunable	[23,24,25,27,40]
HiPIMS	Peak current > 1 A/cm2; Ar/O2 mix	RT–500	5–20 nm/min	Compact films, textured layers, columnar growth	Controlled Vₒ	[9,49,62,63]
Spark/Laser in Liquid	0.5–3 kV or 1–3 J/cm2 laser, single-pulse	Ambient	109–1011 part./min	Nanorods, nanosheets, nanowires, clusters	High defect density	[3,4,46,47]
MAO	100–300 V, pulsed discharge	<200	Variable	Porous oxide layers	High Vₒ, good adhesion	[5,48]
Selective Laser Oxidation	Pulsed-periodic laser, Cu–Zn target, air	<700	1–4 µm/min (1D nanowires)	Nanowires, nanosheets, 2D patterns	Diffusion-driven Vₒ	[10,45,57]
Hybrid (PLD + RFMS/PLD + HiPIMS)	Dual-source; plasma tuning	RT–650	Tunable	Doped/architectured nanostructures	Complex control	[64,65]

* All temperatures are given in °C.

summarizes representative pulsed ZnO synthesis techniques. PLD ensures stoichiometric transfer and high crystallinity; HiPIMS yields dense, defect-controlled films; pulsed laser or spark discharges in liquids form nanorods and nanosheets under ambient conditions; MAO generates adherent porous oxides; pulsed laser oxidation enables diffusion-driven nanowire formation; and hybrid plasma–laser systems combine morphological and compositional control. Each technique offers distinct advantages in morphology, crystallinity, and defect tuning, governed by pulse dynamics and ambient atmosphere.

This comparison highlights the importance of quantitative modeling and data-driven optimization to efficiently explore the complex parameter landscape of pulsed ZnO synthesis. Variations in energy input, environment, and transformation kinetics collectively define ZnO morphology, crystallinity, and defect structure. Abbreviations: RT—room temperature; Vₒ—oxygen vacancy; part./min—particles per minute; p(O₂)—partial oxygen pressure.

2.4. Reproducibility and Experimental Control

Ensuring reproducibility in pulsed ZnO synthesis requires systematic calibration, environmental control, and standardized data management. For lasers, energy meters and reference targets verify fluence [66]; for electrical discharges, calibrated current and voltage diagnostics ensure accurate pulse measurements [67]. Regular monitoring of targets and electrodes prevents systematic drift due to wear and material redeposition [68]. Atmospheric stability is equally critical. Precise control of Ar/O₂ flows via mass flow controllers and continuous pressure logging maintains reproducible plasma conditions [69]. Chamber pressure influences both adsorbate monolayer formation and gas mean free path (Figure 5). In-process plasma diagnostics, such as optical emission spectroscopy (OES) and residual gas analysis, enable real-time monitoring of the oxygen balance and help distinguish between metallic and oxide growth regimes [70]. Even trace levels of humidity or residual impurities can modify the defect chemistry of ZnO, underscoring the need for continuous atmosphere control and diagnostics [71].

Substrate preparation plays a crucial role in achieving reproducible ZnO growth. Standardization of cleaning procedures, buffer layer deposition (e.g., Ti, Cr, or oxide layers of 5–20 nm), and mounting methods is essential, as these factors determine film adhesion and thermal stability [72,73]. Deposition parameters, particularly substrate temperature and pulse-controlled energy input, govern the resulting morphology of ZnO thin films. SEM images in Figure 6 demonstrate how increasing deposition temperature affects columnar size and uniformity, highlighting the influence of thermal and energetic conditions on film structure.

Thermal monitoring is particularly important in pulsed ZnO synthesis. Time-synchronized measurements using pyrometers, thermocouples, or infrared (IR) cameras enable tracking of transient thermal loads during pulse sequences. Deposition temperature critically influences the defect structure and electronic mobility of ZnO. For instance, in RF-sputtered ZnO films, increasing substrate temperature decreases the concentration of zinc interstitials and oxygen vacancies, thereby enhancing carrier mobility (Figure 7) [74]. It should also be noted that in cases where thermoacoustic phenomena drive selective oxidation—such as in Cu–Zn alloys under periodic laser excitation—both thermal and vibrational parameters must be carefully monitored [75]. Thus, thermal–mechanical integration is essential for stable, defect-controlled synthesis.

Rigorous calibration, thermal management, and standardized process protocols remain central to ensuring reproducibility in pulsed synthesis. Building upon these foundations, closed-loop workflows with automated feedback reduce variability and support adaptive control using active learning and Bayesian optimization [76,77]. Modular “science factory” systems integrate reconfigurable hardware [78], while knowledge-graph frameworks link distributed self-driving laboratories [79,80]. Reproducibility in such systems demands strict operational safety, fault recovery, and long-term data archiving [81]. Complementing these operational requirements, open deposition of raw and processed experimental data (e.g., oscilloscope traces, spectra, thermograms) to repositories with persistent identifiers (e.g., Materials Data Facility, Zenodo) ensures FAIR compliance, while machine-readable packaging (RO-Crate) facilitates interoperability and integration into automated pipelines [82].

In summary, pulsed synthesis enables controlled fabrication of ZnO nanostructures through precise, localized energy delivery. Achieving reproducibility requires rigorous calibration, thermal management, standardized protocols, and structured digital records. These practices form the data foundation for subsequent AI-driven optimization and closed-loop control of ZnO pulsed synthesis.

3. Preparation and Reporting of Experimental Data for AI Optimization

3.1. Principles of Experimental Data Preparation in Pulsed Synthesis of ZnO

Preparation of experimental data for AI-guided pulsed synthesis of ZnO nanostructures on thin metal shells requires rigorous metrological protocols, reproducibility controls, and standardized metadata. Pulsed deposition techniques such as PLD and HiPIMS operate under inherently dynamic conditions: fluctuations in plasma density, ablation rates, and transient energy fluxes directly affect measurement fidelity and reproducibility. In ZnO nanostructure growth, even small variations in plume propagation or plasma–substrate coupling can lead to measurable changes in morphology, defect density, or optoelectronic response.

To ensure suitability for AI-driven modeling, raw experimental data must include uncertainty analyses, calibration records, and contextual metadata to guarantee traceability and reproducibility. International standards such as ISO/IEC Guides [83,84] provide a framework for reporting statistical (Type A) and systematic (Type B) uncertainties that can affect ZnO synthesis outcomes.

In pulsed ZnO synthesis, uncertainties propagate nonlinearly due to pulse-to-pulse variations, chamber instabilities, and local plasma–substrate fluctuations. Classical error-propagation methods are often inadequate; stochastic approaches, such as kinetic Monte Carlo simulations of layer growth during PLD [85], better capture non-Gaussian uncertainty in multiscale plasma–substrate systems.

Traceability relies on calibration protocols linking measurements to recognized standards. For ZnO nanostructures, this includes certified reference samples for thickness calibration, regularly validated profilometry, and standardized techniques such as ellipsometry for optical constants. Small deviations in laser alignment, chamber geometry, or magnetron configuration may introduce systematic biases, which can be addressed using digital calibration frameworks validated against reference measurements [86,87].

The integration of uncertainty-aware workflows is essential for making ZnO datasets usable in AI-driven optimization [87]. Machine learning approaches such as Bayesian neural networks, ensemble methods, and probabilistic regression require training datasets that are accompanied by rigorously reported uncertainty values. Figure 8 [88] summarizes fundamental methods for evaluating the quality of measurement systems and results, including repeatability, reproducibility, bias, calibration, and traceability. Applying these procedures ensures that experimental datasets for pulsed ZnO synthesis are reliable and suitable for AI-driven modeling and optimization. When combined with reproducible calibration protocols, such datasets enable consistent interpretation of experimental results and provide a reliable foundation for establishing structure–property relationships in ZnO thin films synthesized by pulsed laser deposition [89].

3.2. Standards for Data Recording and Metadata Representation

Reliable AI-driven optimization of pulsed ZnO synthesis critically depends on consistent application of standards for data recording and metadata representation. Inconsistencies in units, variable naming, or incomplete metadata can hinder interoperability and diminish the utility of otherwise high-quality datasets. In pulsed methods such as PLD and HiPIMS, the transient and nonlinear nature of plasma–substrate interactions compounds measurement uncertainty, making rigorous documentation of contextual descriptors—including substrate type, chamber geometry, laser spot size, and pressure dynamics—essential [90,91].

Best practices for pulsed ZnO synthesis emphasize three complementary requirements. First, primary measurements (e.g., oscilloscope traces of plasma–target interactions, in situ plasma diagnostics, thickness profiles of ZnO films) should be clearly separated from derived quantities (e.g., fluence, transient surface temperature) and from contextual metadata (substrate composition, cooling method, calibration status). Second, all parameters must be expressed in SI units and encoded using controlled vocabularies to avoid ambiguity during automated parsing. Third, machine-readable packaging—such as JSON-LD (JavaScript Object Notation for Linked Data) schemas or RO-Crate (Research Object Crate) bundles—ensures that ZnO datasets remain self-describing and directly ingestible into FAIR workflows. Concrete examples include detailed metadata guidelines for PLD processes [69] and automated extraction of deposition parameters using natural language processing [92].

Although these principles were originally developed in genomics and chemistry, they are increasingly being adapted to materials science. For pulsed ZnO synthesis, assembling experimental results as self-contained archives—combining raw diagnostics, derived data tables, and provenance manifests—reduces time-to-reuse in AI workflows. This enables automated pipelines to select ZnO datasets that match specific boundary conditions, such as pulse energy or substrate curvature, thereby facilitating Bayesian optimization and surrogate modeling [93,94].

3.3. Representativeness and Scalability of Experimental Data

Datasets for AI optimization must also be representative of the variability encountered when scaling ZnO pulsed synthesis from laboratory experiments to industrial production. While laboratory-scale PLD studies often operate on substrates of only a few square millimeters under highly controlled conditions, industrial HiPIMS or PLD systems typically process much larger wafers, where geometric non-uniformities, transient instabilities, and heterogeneous substrates significantly affect growth outcomes [95,96].

To bridge this gap, multi-fidelity experimental design is increasingly adopted: (i) broad screening on small-scale samples to map the parameter space, (ii) medium-fidelity validation on intermediate geometries, and (iii) high-fidelity pilot experiments under near-industrial conditions. Such hierarchical datasets enable AI models to incorporate boundary effects and transfer knowledge across different scales [97]. Figure 9 illustrates a representative example of multi-fidelity data, showing low-fidelity (LF) and high-fidelity (HF) data points, the true response function, and the predictions obtained via additive, multiplicative, and comprehensive correction methods [97]. This highlights how combining LF and HF datasets can enhance AI model predictions and capture boundary effects when scaling up synthesis processes.

Capturing contextual metadata such as cooling efficiency, mounting geometries, and flow-field asymmetries is particularly important, since these factors introduce covariates that strongly influence ZnO growth kinetics. For example, asymmetrical cooling during HiPIMS may lead to oxygen deficiency gradients across the substrate, which must be documented to avoid confounding in AI models. Furthermore, the deliberate introduction of controlled heterogeneities (e.g., pressure gradients, flow perturbations) during laboratory campaigns has been shown to improve the robustness of surrogate models against non-idealities arising in industrial environments [69].

3.4. Quality Control and Measurement Accuracy

The reliability of AI-driven optimization depends fundamentally on the accuracy and reproducibility of experimental measurements. Systematic quality control (QC) and transparent uncertainty quantification are therefore indispensable in ZnO pulsed synthesis research. Traceable calibration procedures for profilometry, ellipsometry, and in situ plasma diagnostics are essential to ensure comparability between different laboratories [98].

Uncertainty evaluation should follow established metrological standards, ensuring that both statistical fluctuations and systematic contributions are explicitly addressed [90,99]. A fully balanced two-stage experimental design allows for the evaluation of contributions from different sources of variance (e.g., measurement repeatability and primary sampling). The duplicate sampling scheme is shown in Figure 10. For highly dynamic plasma environments, Gaussian error propagation may underestimate uncertainties; advanced approaches such as Monte Carlo propagation or bootstrapping provide more realistic assessments [100].

In situ QC has become integral to pulsed-laser deposition workflows, where real-time diagnostics of composition and growth dynamics provide immediate feedback on process stability. These measurements enable identification of compositional drifts and their correlation with deposition parameters, supporting consistent structural quality and reproducibility [101]. Building on this foundation, autonomous PLD platforms increasingly incorporate these monitoring channels into feedback loops, linking calibration data with adaptive process control. Such integration ensures long-term stability of synthesis parameters and minimizes cumulative errors in AI-guided closed-loop experiments [98]. Consistent evaluation and reporting of measurement uncertainty further strengthen this framework, allowing machine learning models to interpret experimental variability correctly and to identify genuine structure–property relationships rather than artifacts of measurement noise [102].

3.5. FAIR Principles and Open Data Repositories

The FAIR principles provide a unifying framework for structuring ZnO pulsed synthesis datasets to maximize their value for AI-driven workflows. Implementation requires persistent identifiers, standardized metadata schemas, and packaging conventions that bind data and provenance into cohesive, machine-readable units [103].

Recent infrastructures such as NOMAD and FAIRmat have demonstrated the transformative role of community-driven repositories in integrating heterogeneous experimental and computational datasets [104,105]. Materials Cloud further extends this paradigm by combining data storage with reproducible workflows that enable transparent dissemination of results [99]. Lightweight frameworks such as RO-Crate simplify FAIR compliance by embedding metadata directly within archives, lowering the barrier for laboratory adoption [106].

Deposition of datasets into such infrastructures ensures long-term accessibility and supports federated AI workflows, where models can automatically query distributed repositories. Importantly, FAIR-aligned practices are also entering industrial contexts: digital platforms for research data management and workflow integration [107] exemplify how laboratory-scale experiments can be connected to industrial databases, enabling smoother technology transfer and accelerating the translation of ZnO-based coatings from research to manufacturing [108].

Collectively, preparation and reporting of experimental data for pulsed ZnO synthesis require more than routine characterization; they demand rigorous uncertainty evaluation, standardized metadata, and quality-controlled workflows. Standards such as ISO/IEC guidelines and FAIR principles provide the methodological backbone for ensuring reproducibility and interoperability, while infrastructures like NOMAD, Materials Cloud, and FAIRmat transform heterogeneous datasets into usable resources for AI-driven optimization. By embedding traceable calibration, metadata-rich reporting, and FAIR-aligned dissemination, ZnO synthesis research can serve as a model for broader materials science domains where AI-guided experimentation is emerging as a transformative paradigm.

When applied to pulsed ZnO synthesis, FAIR-aligned data structures combined with ISO-based uncertainty evaluation provide a practical pathway for enhancing reproducibility and model reliability. By documenting each synthesis pulse and associated diagnostics—such as pulse energy or power, temporal profile, plasma emission (OES/ICCD), and substrate temperature—together with clearly documented uncertainty estimates for key process parameters, including substrate temperature, ambient conditions, and pulse energy, datasets capture both inherent process variability and calibration context. Structured metadata, formalized within RO-Crate bundles and consistent with ISO/GUM uncertainty practices [83,84,85,86,87,90,91,106], can be automatically ingested by probabilistic AI models, including Gaussian processes and Bayesian neural networks. This uncertainty-aware integration enables more stable, transferable, and interpretable optimization workflows, directly linking rigorous data standards to reproducibility and AI-guided process control in pulsed ZnO synthesis.

4. Artificial Intelligence Methods for Process Optimization and Control

4.1. Active Learning and Adaptive Sampling

Active learning (AL) and adaptive sampling strategies are increasingly regarded as promising approaches to accelerating research in materials science. Unlike traditional design of experiments (DoE) methods, which rely on uniform or predefined sampling, AL focuses on identifying the most informative regions in the parameter space based on uncertainty estimates. This approach increases the efficiency of data collection and directs experimental efforts toward regions most critical for improving predictive models. Such a principle makes AL particularly effective for multi-parameter processes typical of pulsed synthesis of nanomaterials, including PLD and HiPIMS.

AL has been successfully applied to construct composition–property maps and explore complex phase diagrams with limited data [109]. A closely related approach, Bayesian optimization, employs probabilistic surrogate models, often based on Gaussian Processes, to iteratively select experimental conditions through acquisition functions [110,111]. Both methods aim to minimize experimental effort while improving predictive accuracy.

In the context of ZnO nanostructure synthesis, AL and Bayesian optimization provide a systematic framework for tailoring morphology and functional performance. For instance, in PLD, controlled variation in substrate temperature and ambient pressure determines the crystallinity, alignment, and optical characteristics of vertically oriented ZnO nanorods grown by high-pressure assisted deposition [112]. Similarly, picosecond pulsed laser deposition performed in burst mode allows for precise modulation of film microstructure and optical response by tuning pulse train parameters [113]. Embedding AL into such workflows enables efficient exploration of parameter spaces and rapid identification of optimal synthesis regimes.

Beyond laboratory studies, these methods are increasingly being integrated into cyber-physical manufacturing systems. As shown in [114], the combination of AI-driven optimization with deposition technologies supports adaptive control in smart manufacturing environments. Moreover, the integration of machine learning with in situ diagnostics, such as enhanced recording of plasma clouds using CCD cameras (ICCD), enables real-time monitoring of processes and anomaly detection, providing closed-loop process control [115] (Figure 11). While Figure 11 presents a general CNN-based workflow for plasma imaging, in the context of ZnO pulsed synthesis the diagnostic inputs could include ICCD sequences of ZnO plasma plumes, synchronized with substrate temperature, oxygen partial pressure, and laser pulse energy. CNN predictions would then inform adaptive control strategies to optimize ZnO nanostructure growth kinetics and morphology.

Taken together, AL and Bayesian optimization establish a methodological basis for moving from trial-and-error experimentation toward adaptive, data-driven optimization frameworks in Artificial Intelligence-guided pulsed synthesis of ZnO nanostructures on thin metal shells.

4.2. Physics-Informed Neural Networks (PINNs)

PINNs combine machine learning with governing physical equations by embedding conservation laws and differential operators directly into the training process. This integration ensures predictive robustness, interpretability, and extrapolation capabilities beyond the training data—qualities particularly relevant for plasma-assisted pulsed synthesis methods such as PLD and HiPIMS [116,117].

A key advantage of PINNs is their ability to reconstruct hidden or partially observable physical fields from sparse or noisy diagnostics. For example, Parareal PINN (PPINN) architectures have been shown to efficiently solve time-dependent partial differential equations, a property highly relevant for capturing the transient nonequilibrium dynamics of laser-induced plasmas [118]. Low-temperature plasma simulations based on PINNs already demonstrate feasibility in reproducing transient behaviors with limited experimental input [119], providing a transferable framework for modeling plasma plume evolution and its interaction with metallic substrates during ZnO nanostructure growth.

PINNs have also been employed for surrogate modeling of thermal fields. In [120], a framework was proposed to reconstruct three-dimensional temperature distributions in laser-driven processes without relying on precomputed reference data, demonstrating direct applicability to pulsed laser ablation and plasma-assisted ZnO synthesis. Such surrogate models enable better regulation of crystallization kinetics and morphological evolution, improving controllability of nanostructure formation on thin metal shells.

To further enhance predictive capabilities, PINNs are often combined with additional machine learning strategies. Regression-based approaches are used to estimate material properties under manufacturing constraints, as illustrated by the generalized scheme of regression-based predictive modeling (Figure 12), which includes data collection, cleaning, grouping, exploratory analysis, model building, and model evaluation [121]. Ensemble learning, in turn, improves accuracy and robustness in nonlinear plasma-assisted processes [122]. Moreover, advanced evaluation metrics, such as the coefficient of determination (R²), provide a more informative assessment of PINN models with regression enhancement compared to traditional error indicators [123]. The regression-based modeling framework illustrated in Figure 12 can be applied to ZnO synthesis by using limited in situ measurements (e.g., plasma emission, surface temperature) to reconstruct transient physical fields via PINNs, which then guide the choice of deposition parameters to achieve desired film crystallinity and defect states.

These developments indicate a gradual transition from proof-of-concept studies toward practical predictive-control tools, where multiscale and multiphysics coupling—including plasma dynamics, heat transfer, mass transport, and fluid-dynamic effects—forms the basis for adaptive optimization of AI-guided pulsed synthesis of ZnO nanostructures on thin metal shells.

4.3. Graph Neural Networks (GNNs) for Microstructure and Properties

GNNs are emerging as powerful tools for modeling non-Euclidean data structures, making them highly suitable for representing crystalline lattices, grain boundaries, and polycrystalline morphologies. Their ability to capture relational dependencies between atoms, grains, or domains opens avenues for predictive modeling of microstructure and functional properties in oxide nanostructures [124].

Early implementations, such as the Crystal Graph Convolutional Neural Network (CGCNN), demonstrated accurate prediction of material properties directly from crystallographic data [124]. On this basis, specialized models have been developed. GrainNN, a model based on recurrent Long Short-Term Memory (LSTM) networks that incorporate information about neighboring grains, predicts the evolution of polycrystalline structures with high accuracy [125], whereas GrainGNN extends these capabilities to three-dimensional dynamic modeling of grain growth, providing accuracy comparable to phase-field modeling, but at lower computational cost [126]. Interpretability-oriented approaches have also emerged, in which graph-based methods are used to construct representations of polycrystalline microstructures, enabling explicit links between their topological features and macroscopic mechanical properties [127].

Pulsed-synthesized ZnO nanostructures on thin metal substrates are typically polycrystalline, consisting of multiple crystallites with varying orientations. This makes their microstructure suitable for analysis and property prediction using graph-based models—GNNs—that account for interactions between neighboring grains. Each grain is represented as a node, with physical features including grain size, orientation (Euler angles), and the number of neighboring grains. The adjacency relations between grains are encoded in a graph structure, allowing the graph neural network to capture inter-grain interactions critical for predicting macroscopic properties. Such GNN models can efficiently predict the impact of microstructural features on functional outcomes, including mechanical stability, defect formation, and growth anisotropy, providing a quantitative link between synthesis parameters and the resulting ZnO microstructures (Figure 13). Although Figure 13 shows a general graph-based approach, it illustrates the type of representation that can be applied to ZnO nanostructures to connect deposition conditions with microstructural and functional properties.

In the context of pulsed synthesis of ZnO nanostructures on thin metal shells, GNNs can serve as tools for linking process parameters to microstructural outcomes. For example, synchronized HiPIMS with controlled ion acceleration enables the growth of functional ZnO-based layers at reduced substrate temperatures [128]. Data obtained from such experiments provide a basis for training graph-based models capable of predicting grain distribution, defect formation, and the emergence of structural inhomogeneities. Similarly, in PLD, the complex dynamics of plasma plume–substrate interactions create additional opportunities for applying artificial intelligence–based modeling approaches.

The role of AI in manufacturing is steadily expanding, from process monitoring and optimization [129] to the integration of machine learning algorithms into industrial workflows [130]. This shows that graph-based approaches extend beyond academic research and are becoming practical tools for connecting process design with functional outcomes. GNNs have already demonstrated effectiveness in predicting properties at both atomic and microstructural scales, including phase stability and grain boundary behavior [131].

Thus, GNNs provide a versatile platform for establishing links between deposition conditions and the structural–functional characteristics of ZnO nanostructures synthesized on thin metal shells, supporting their integration into digital workflows for materials design.

4.4. Self-Driving Laboratories and Reinforcement Learning

Reinforcement learning (RL) provides a systematic framework for adaptive control in dynamic and nonlinear systems, where conventional feedback approaches face limitations. Its effectiveness has been demonstrated in diverse domains, including stabilization of nonlinear regimes in self-tuning fiber lasers [132], low-latency control of ultrafast laser dynamics [133], real-time feedback regulation in quantum systems [134], and adaptive thermal management in laser powder bed fusion [135]. Together, these studies highlight the potential of RL for processes characterized by rapid transients and complex variability. PLD and plasma-assisted synthesis of ZnO nanostructures are characterized by rapid transients, strong nonlinearities, and high sensitivity to process parameters. Plasma–surface interactions evolve on short timescales, exhibit strong nonlinearity, and are highly sensitive to external parameters. In such settings, RL can be coupled with in situ diagnostics—such as optical emission spectroscopy, time-resolved plume imaging, or reflectometry—to establish adaptive strategies for process control. Instead of reward functions in a narrow sense, performance metrics can be defined in terms of growth stability, reproducibility, or structural quality, enabling optimization toward desired material outcomes.

Integration with model-based and safety-aware RL approaches, supported by digital twin frameworks, provides additional opportunities to accelerate learning and ensure robustness. Such strategies can guide pulsed synthesis of ZnO nanostructures on thin metal shells, linking real-time experimental feedback with autonomous decision-making in materials design. The integration of RL with self-driving laboratories represents a transformative step in materials science. Frameworks for autonomous experimentation have been introduced in [109] and further extended in [136], where AI-driven agents iteratively design, execute, and analyze experiments. These systems gain additional robustness when RL is combined with digital twin models, as highlighted in [137,138], enabling predictive feedback loops that support adaptive optimization of synthesis processes.

Figure 14 illustrates the autonomous laboratory management process [138]. The framework shows the closed-loop integration of a physical experimental setup (e.g., pulsed synthesis of ZnO nanostructures), real-time in situ diagnostics, a digital twin for simulation and predictive modeling, and a reinforcement learning agent that iteratively selects experimental actions. This architecture enables continuous adaptation of process parameters to optimize key outcomes, including growth stability, reproducibility, and structural quality of the material. While Figure 14 represents a general digital twin–RL workflow, in the context of ZnO pulsed synthesis the state vectors could include real-time diagnostics such as OES spectra, ICCD images of the plasma plume, and substrate temperature; the actions correspond to laser fluence, pulse frequency, ambient oxygen pressure, and substrate conditions; the reward function may combine metrics of crystallinity, defect density, and growth uniformity. This interpretation links the general framework directly to the practical control of ZnO nanostructure growth.

Taken together, RL provides a pathway toward fully autonomous experimental platforms, where conditions of pulsed synthesis of ZnO nanostructures on thin metal shells are continuously optimized based on in situ diagnostics and predictive models, supporting reproducible fabrication and controlled tailoring of structural features.

4.5. Digital Loop and AI-Guided Workflow for ZnO Nanostructure Synthesis

A defining trend in modern materials research is the convergence of experimental and computational data streams into self-improving feedback cycles, commonly referred to as the digital loop. In this paradigm, predictive models guide the design of experiments, while experimental data iteratively refine the models, creating an adaptive model–experiment–optimization workflow [136]. At the core of this approach lies the digital twin—a dynamic virtual counterpart of a physical system that evolves in real time as new data become available. Studies demonstrated its potential for predictive manufacturing and process monitoring [139,140], and later developments formalized its use in fully closed model–experiment–optimization frameworks [141]. An illustrative example of such a data-driven digital twin, implementing a closed feedback loop between physical experiments, machine-learning models, and operator decision-making, is shown in Figure 15 [142]. These frameworks have been successfully applied in predictive maintenance for smart manufacturing and system-level performance monitoring in production lines [143], while advanced methods for real-time parameter updating ensure adaptability under dynamically changing operating conditions [144]. In the ZnO scenario, the closed-loop digital twin framework depicted in Figure 15 would integrate measurements from plasma diagnostics and temperature sensors with predictive ML models (RF, XGBoost, PINN, GNN) to iteratively optimize deposition conditions, accounting for uncertainties in fluence, temperature, and growth kinetics. Reinforcement learning has been coupled with digital twin architectures to create adaptive predictive-feedback frameworks [137], and rigorous metrological standards ensure trustworthy virtual experiments and cyber-physical integration [114].

For the synthesis of ZnO nanostructures on thin metallic substrates, the described AI methods demonstrate potential practical value, based on successful implementations for related thin-film materials. The workflow may include the following steps: first, Bayesian optimization (e.g., via the COMBO library) is used to select optimal deposition parameters—such as pulse energy, pulse frequency, ambient pressure, and substrate temperature—to achieve the desired crystallography and morphology. In parallel, PINNs can reconstruct the three-dimensional distribution of temperature, plasma density, or other relevant physical fields from limited in situ diagnostics (e.g., ICCD imaging of the plasma plume), allowing for the prediction of local growth conditions. The resulting fields can then be fed into a GNN (e.g., GrainGNN) to predict grain structure formation, defect distribution, and microstructural evolution in the ZnO thin film on the metallic substrate. Based on these predictions, a reinforcement learning agent adaptively adjusts synthesis parameters in real time, selecting actions that maximize growth stability and target material properties. This example demonstrates the practical integration of AI frameworks to guide adaptive synthesis of ZnO nanostructures, translating conceptual methods into a workflow applicable to laboratory-scale pulsed deposition processes.

5. Synthesis and Applications of ZnO Nanostructures

5.1. Pulsed Deposition Strategies for ZnO Nanostructures

Pulsed physical deposition methods—PLD, HiPIMS, and other time-modulated sputtering techniques—offer exceptional flexibility for controlling the synthesis of ZnO nanostructures and thin films [145,146]. In these methods, the transient energy input into the target–plasma–substrate system can be finely tuned in time and space, enabling dense, crystalline, and defect-engineered ZnO layers even at relatively low substrate temperatures. This dynamic regime differs fundamentally from conventional continuous (direct-current or RF) sputtering, where steady-state plasmas provide limited control over instantaneous ion energy and flux.

Femtosecond- and nanosecond-pulse approaches extend this control to the micro- and nanoscale, allowing ZnO to be structured directly or grown via intermediate metallic states. For instance, a two-step route combining ultrafast laser structuring of metallic Zn and subsequent low-temperature oxidation yields large-area ZnO micro- and nanostructures with excellent crystalline order and luminescent response [146]. Likewise, pulsed magnetron sputtering enables the formation of ZnO microrods and hybrid Ag/ZnO heterostructures, where modulation of the pulse frequency and duty cycle governs grain alignment, exciton–plasmon coupling, and optical performance [147].

In thin-film growth, PLD provides further advantages. When PLD is performed at room temperature on flexible cyclo-olefin polymer substrates, pretreatment with short-wavelength UV (172 nm) excimer-lamp irradiation in air improves ZnO film crystallinity and surface uniformity. The process creates reactive oxygen species that mildly oxidize the surface and enhance film adhesion, enabling high-quality ZnO layers on heat-sensitive substrates [148]. The technique also supports precise control over polarity and epitaxy: using Nd:YAG-based PLD, the Zn- and O-terminated orientations of ZnO films can be selectively stabilized, significantly affecting defect incorporation and electronic behavior [149]. Furthermore, controlled adjustment of laser fluence during PLD on porous silicon promotes the transition from discrete nanoparticles to continuous nanocrystalline layers, enhancing photodetector performance [150].

Beyond laser-based routes, pulsed electron deposition (PED) enables fabrication of ZnO films with adjustable oxygen deficiency—spanning from transparent crystalline to black amorphous phases—through modulation of pulse energy and ambient pressure [151]. Together, these examples demonstrate that pulsed synthesis strategies provide access to a wide morphological and electronic spectrum of ZnO materials, establishing a flexible platform for AI-guided optimization and integration on thin metal shells.

5.2. Morphology-Controlled Functional Performance of ZnO Nanostructures Synthesized by Pulsed Techniques

The functional performance of ZnO nanostructures—spanning applications in gas sensing, photocatalysis, and optoelectronics—is strongly governed by morphology, crystallinity, and defect chemistry, all of which are directly tunable through pulsed deposition parameters. By adjusting pulse frequency, duty cycle, or substrate temperature, it is possible to modulate grain orientation, density, and surface states, thereby optimizing charge transport and interfacial reactivity.

Systematic studies of ZnO deposited by HiPIMS have shown that increasing substrate temperature leads to enhanced crystallinity, smoother morphology, and improved optical transmission, reflecting a strong coupling between energetic ion flux and film ordering [152]. Comparative analyses between HiPIMS and conventional RF sputtering indicate that the transient high plasma density in HiPIMS produces more compact microstructures with improved electrical conductivity [153]. High-quality ZnO layers fabricated by pulsed sputtering also exhibit tailored work functions and stable photoelectrochemical responses, confirming the role of controlled pulse dynamics in defining optoelectronic behavior [154].

Beyond thin films, pulsed methods have been successfully employed to engineer three-dimensional architectures. Vertically aligned hexagonal ZnO microcrystals synthesized by magnetron sputtering demonstrate stimulated emission and enhanced light–matter interaction due to their ordered columnar morphology [155]. Likewise, PLD performed under controlled reactive atmospheres enables fine-tuning of gas-sensing sensitivity via surface defect engineering and modulation of oxygen vacancy concentration [156]. Femtosecond-laser-assisted processing of Zn foils has also been shown to generate oxygen-deficient ZnO nanostructures with improved photocatalytic activity, where mid-gap states facilitate visible-light absorption and charge separation [146].

Overall, pulsed deposition techniques provide a means for controlled tailoring of ZnO structure and properties. By adjusting energy parameters, plasma composition, and local thermal conditions, it becomes possible to obtain nanostructures—from compact thin films to hierarchical 3D architectures—with predefined optoelectronic, catalytic, and sensing functionalities.

5.3. Technological Advantages and Design Flexibility in Pulsed Synthesis of ZnO Nanostructures

Compared with continuous deposition or chemical growth techniques, pulsed synthesis methods provide distinctive advantages for controlling structure formation and functional properties of ZnO-based materials. First, the rate of material delivery and the energy input can be adjusted independently, enabling controlled formation of dense epitaxial layers [156] as well as oxygen-deficient amorphous structures [146]. The temporally confined energy input promotes nonequilibrium growth regimes, facilitating the design of tailored crystallinity and defect distributions.

Second, the localized and time-resolved nature of pulsed energy delivery—whether through plasma discharges or laser–matter interaction—allows spatial patterning and nanoscale interface engineering, which are essential for advanced device architectures. Moreover, pulsed approaches exhibit intrinsic compatibility with thermally sensitive and flexible substrates. For instance, femtosecond-laser processing and PLD of ZnO on polymeric or metallic supports enable improved structural ordering without exceeding substrate softening temperatures [146,156]. Similarly, HiPIMS reduces the overall thermal load because most of the energy is delivered in short bursts [157]. This makes pulsed techniques particularly suitable for flexible and transparent electronics, wearable sensors, and optoelectronic components.

Furthermore, synchronization of pulsed energy sources with substrate bias or auxiliary plasma activation offers additional degrees of control over interfacial composition and stress state, enabling precise engineering of heterostructures and multilayer oxide–metal systems. In this case, spatiotemporal modeling of laser-induced temperature fields provides quantitative insight into the transient heating and cooling dynamics that determine nucleation and growth during laser-driven synthesis [158].

Beyond conventional vacuum deposition, pulsed synthesis has also been extended to reactive and photochemical environments. For example, femtosecond-laser-assisted photochemical formation in glycerol-containing media produces patterned ZnO nanostructures with tunable grain morphology [159]. In addition, sputtering from compositionally complex multi-element powder targets introduces new opportunities for designing ZnO-based coatings with adjustable stoichiometry and multifunctional response [160].

5.4. Scaling and Doping Strategies for ZnO in Pulsed Processing

Scaling pulsed synthesis from laboratory to industrial dimensions requires maintaining uniform film thickness, composition, and defect distribution across extended substrates. Multi-beam and high-repetition-rate PLD configurations enable deposition of ZnO coatings with consistent stoichiometry and morphology over large areas [161]. Stable plasma–substrate interactions at elevated pulse frequencies further ensure reproducible growth [162]. Modular PLD architectures with adjustable geometries improve process adaptability, facilitating the formation of compositional gradients and parameter mapping for accelerated optimization [163].

Concepts developed for regulating oxygen-related defects in ZnO [151] are being applied to other oxide semiconductors, forming a broader framework for non-equilibrium growth control. By modulating oxygen content and plasma composition across pulse regimes, concentrations of vacancies, interstitials, and dopants can be finely tuned, defining film conductivity, carrier relaxation, and surface reactivity [164,165]. Subsequent pulsed annealing or plasma-assisted treatments stabilize metastable defect configurations and suppress nonradiative recombination centers [166,167].

Doping remains a central strategy for tailoring ZnO functionality. Fluorine incorporation enhances carrier density and photocatalytic performance [168], whereas aluminum and gallium dopants yield transparent conductive films for optoelectronic applications [169,170]. Controlled co-sputtering or HiPIMS from Zn–Al composite targets ensures uniform dopant incorporation without secondary phase formation [171]. Reactive pulsed laser deposition with nitrogen enables modulation of donor–acceptor balance and localized p-type conductivity, though further progress is required [172]. Pulsed plasma and laser-assisted activation techniques provide efficient dopant incorporation with minimal thermal diffusion, preserving electronic uniformity [173,174].

In femtosecond laser–assisted systems, spatial and temporal localization of energy offers additional degrees of control over composition and microstructure. Direct laser patterning of ZnO regions on polymeric or metallic substrates allows for the creation of functional elements without exceeding the thermal stability limits of the underlying materials [175]. Localized oxidation, alloying, and phase transformation, driven by focused pulsed irradiation, enable selective modification of ZnO and related oxide systems with minimal heat-affected zones [176,177]. These approaches extend pulsed synthesis beyond vacuum-based processing, providing routes to local structure control and interface modification under atmospheric conditions [178,179].

Atmospheric-pressure pulsed plasma and oxidation processes further enhance the versatility of pulsed processing. They make it possible to deposit uniform ZnO coatings and doped layers directly on flexible or thermally sensitive substrates while maintaining phase stability and a controlled defect structure [177]. High-energy pulsed sputtering using alloyed or compositionally complex targets ensures precise adjustment of stoichiometry and dopant incorporation, which is critical for developing multifunctional ZnO-based coatings with tailored optical and electrical properties [170,180].

Pulsed processing thus combines temporal precision and spatial selectivity with scalability and compositional control. The same principles that determine defect dynamics at ultrafast timescales can be applied to longer pulse regimes, enabling phase transformation, annealing, and dopant activation within controllable nonequilibrium intervals. This synthesis flexibility forms the foundation for the development of ZnO-based heterostructures and multifunctional coatings for next-generation optoelectronic, catalytic, and sensing applications.

The presented results demonstrate that pulsed synthesis provides a robust and adaptable framework for the controlled fabrication of ZnO nanostructures with tunable morphology, crystallinity, and defect chemistry. Through precise manipulation of pulse energy, temporal parameters, and local thermal dynamics, it enables accurate control of growth regimes, doping, and electronic structure across diverse substrates, including flexible supports. The complementary pulsed techniques—PLD, HiPIMS, PED, and laser-assisted processing—offer distinct advantages in energy delivery and scalability. When coupled with real-time diagnostics and AI-driven optimization, pulsed synthesis emerges as a powerful technological platform for the scalable design of advanced ZnO-based coatings, heterostructures, and multifunctional devices.

6. Discussion and Perspectives

6.1. AI Control and Experimental Challenges in Pulsed ZnO Synthesis

Compared with continuous deposition or chemical synthesis techniques, pulsed methods provide fundamentally distinct routes to non-equilibrium structure formation. Conventional PVD and CVD approaches typically rely on steady-state fluxes, where thermodynamic factors dominate nucleation and growth [1,21]. In contrast, the temporally modulated energy input characteristic of PLD, HiPIMS, and related techniques enables dynamic control of instantaneous ion energies, flux densities, and surface reaction kinetics [54,69,86]. This makes it possible to synthesize ZnO nanostructures with high crystallinity and controlled defect states even at low substrate temperatures, which is particularly advantageous for flexible and temperature-sensitive device platforms [148,156].

The integration of AI-based optimization and modeling tools into pulsed synthesis research represents a transition from empirical, parameter-driven optimization toward predictive, data- and physics-informed process control [13,109]. Conventional optimization typically relies on heuristic parameter tuning, whereas AI methodologies—especially active learning (AL), Bayesian optimization, and PINNs—enable systematic exploration of multidimensional process spaces guided by validated models [13,14,109,110]. AI-assisted pulsed synthesis thus combines the precision of non-equilibrium plasma or laser-based control with adaptive model feedback characteristic of intelligent manufacturing environments [137,139,140].

Despite considerable progress, several challenges hinder the full implementation of AI-driven pulsed synthesis. A key limitation is the lack of high-quality, standardized datasets describing transient plasma–surface dynamics and time-resolved growth behavior [92,99,104]. The generation of reliable experimental data remains resource-intensive, and inconsistencies in metrology and data annotation reduce model generalizability and reproducibility [83,84,88,90].

In situ and operando diagnostics—such as time-resolved optical emission spectroscopy, interferometric monitoring, and plasma mass spectrometry—are essential for obtaining reference data necessary for model calibration and validation [70,86,101]. However, their integration into highly dynamic pulsed plasma environments remains technically demanding [67,69,153]. Moreover, the reproducibility of synthesis outcomes is influenced by subtle variations in energy delivery, surface chemistry, and ambient conditions, which complicates both experimental standardization and digital twin synchronization [136,137,139,141,144].

Addressing these issues requires unified data protocols, uncertainty quantification, and hybrid learning frameworks capable of reliable extrapolation beyond the training domain [14,83,103,106]. Together, these aspects define both the potential and the current barriers to implementing AI control in pulsed ZnO synthesis, forming a foundation for the autonomous frameworks discussed below.

6.2. Proposed Experimental Workflow for AI-Guided Pulsed ZnO Synthesis

A modular, stepwise workflow is proposed for AI-guided synthesis of ZnO nanostructures on thin metal shells using pulsed technologies. The workflow integrates rapid low-fidelity screening, active learning and Bayesian optimization, PINN modeling, GNN-based microstructure prediction, high-fidelity validation, and FAIR-compliant reporting. Its primary objective is to accelerate the development of ZnO materials, optimize functional properties such as carrier mobility, defect density, and photoluminescence, and ensure reproducibility through digital data archiving.

Preliminary synthesis experiments are conducted across a broad parameter space, including pulse energy, fluence, repetition rate, gas partial pressure, substrate temperature, and bias potential. Diagnostic data—such as optical emission spectra, ICCD images, and plasma signals—are collected and archived in RO-Crate format along with metadata and uncertainty estimates [106]. Based on the initial datasets, machine learning models, including Gaussian processes, ensembles, and COMBO algorithms, identify promising parameter regions and propose subsequent experiments. Multi-objective acquisition functions guide optimization toward minimizing defect-related photoluminescence or maximizing carrier mobility and structural uniformity [109,122].

PINNs reconstruct temperature fields and plasma dynamics between diagnostic points, providing real-time insight into energy transport and plasma–surface interactions. Deviations from optimal or safe conditions automatically trigger corrective actions, such as fluence reduction, process pausing, or substrate cooling [116,119]. Simultaneously, graph neural networks predict microstructure evolution, including grain distribution and orientation, while reinforcement learning agents adjust process parameters—such as duty cycle, gas flow, or bias—if defect-prone configurations are predicted [126].

Medium- and high-fidelity experiments are then performed to validate the surrogate models and update the digital twin of the synthesis process [110,116]. The digital twin integrates empirical and simulated data, refining predictive accuracy and supporting process scaling for larger deposition areas. All experimental and modeling results, together with process metrics including iteration counts, coefficients of determination (R²), standard deviations, and key performance indicators, are archived in a FAIR-compliant manner to ensure transparency, reproducibility, and data reusability.

Overall, this AI-guided, physics-informed workflow demonstrates a unified approach to accelerating the pulsed synthesis of ZnO nanostructures. By combining autonomous experimentation, digital twin modeling, and systematic data management, it provides a robust foundation for safe, reproducible, and efficient fabrication of high-quality ZnO films and nanostructures.

6.3. AI-Driven Tools and Autonomous Process Control

Future progress in AI-assisted pulsed synthesis will be driven by the integration of machine vision, autonomous experimental platforms, and hybrid AI–physics models. The use of computer vision and deep convolutional networks for real-time monitoring of plasma emission, film morphology, and growth dynamics provides an efficient tool for automated process control and anomaly detection [98,115]. Autonomous experimental systems employing RL agents for adaptive parameter tuning and experimental planning have demonstrated accelerated materials optimization and adaptive experimental design [13,80,110].

The development of hybrid modeling strategies that combine data-driven neural architectures with explicit physical formulations—such as PINNs and GNNs—offers a path toward quantitatively reliable and physically consistent AI models [14,124,125,126]. Their integration into digital-loop and digital-twin architectures, where experimental and simulated data iteratively refine predictive models, establishes a foundation for closed-loop control of synthesis processes in real time [136,139,140,141,144]. Within this framework, the pulsed synthesis of ZnO nanostructures serves as a representative platform linking computational prediction with experimentally validated process control [69,146,156].

Collectively, these developments indicate that the advancement of ZnO synthesis—and functional oxide materials more broadly—will depend on the coordinated evolution of pulsed processing technologies, digital twins, and AI frameworks capable of learning from limited data, quantifying uncertainty, and autonomously optimizing complex synthesis environments [13,99,102,105,136].

To quantitatively assess the effectiveness of AI integration in pulsed ZnO synthesis, several performance metrics can be defined. First, the reduction in the number of experimental iterations required to reach target film properties serves as a direct indicator of optimization efficiency, with typical acceleration factors of 2–10× reported in active-learning and Bayesian-optimization workflows [109,110,115]. Second, model predictive accuracy for morphology or property mapping can be evaluated using statistical measures such as R² and RMSE between predicted and measured values, where R² > 0.8 is generally considered sufficient for practical use [116,117,123]. Third, reproducibility improvements can be quantified as a 20–50% decrease in inter-batch standard deviation (e.g., in film thickness, resistivity, or luminescence intensity) compared with manual operation [101,128,153]. Finally, process-level key performance indicators (KPIs) such as deposition rate (nm · min⁻¹), functional yield, and specific energy consumption provide a basis for benchmarking industrial relevance [129,140,142]. Establishing a standardized “before/after AI integration” reporting format would further enhance comparability across studies and enable transparent evaluation of progress in autonomous ZnO synthesis research [104,105,107].

Overall, integrating AI with pulsed synthesis techniques offers significant opportunities for accelerated materials discovery, yet several critical challenges remain. Limitations in dataset size and quality constrain the training of predictive models, particularly when rare events or subtle structural features must be captured. Latency in measurement, sample transfer, and feedback loops further restrict the practical effectiveness of closed-loop experiments, delaying or distorting real-time decision-making. In addition, discrepancies between simulations and experimental outcomes—the sim-to-real gap—introduce uncertainties that can compromise model-guided optimization. Addressing these barriers requires careful experimental design, rigorous data management, and systematic uncertainty quantification. A realistic assessment of these constraints is essential to guide the development of AI-assisted pulsed synthesis platforms toward reproducible and reliable operation.

7. Conclusions

Pulsed synthesis methods integrated with AI enable controlled formation of ZnO nanostructures on thin metallic and flexible substrates, allowing for the precise regulation of morphology, crystallinity, and defect structures. Pulsed high-energy techniques, such as PLD, HiPIMS, and related plasma- and laser-based approaches, enable non-equilibrium growth control and precise tuning of structural and electronic properties of ZnO nanostructures at relatively low substrate temperatures. Their ability to regulate energy delivery and surface reaction dynamics enables the fabrication of ZnO nanostructures with tunable morphology, crystallinity, and defect structure. Comparative analysis confirms that pulsed synthesis routes offer clear advantages over conventional continuous PVD or CVD methods in terms of process flexibility, reproducibility, and compatibility with temperature-sensitive materials.

Notable achievements include the direct fabrication of conformal ZnO coatings and nanostructures on thin metal shells and flexible metallic foils. These pulsed synthesis methods allow for controlled low-temperature processing, inducing localized oxidation and recrystallization of Zn-bearing layers, which produces high-crystallinity nanostructures with well-defined orientation and tunable defect structures, including adjustable oxygen vacancy concentrations. Machine learning models have been successfully employed to correlate key synthesis parameters, such as pulse energy and frequency, with the resulting morphology and carrier mobility, enabling predictive tuning of synthesis conditions. Together, these developments illustrate the transition from empirical optimization toward data-driven, AI-assisted control of ZnO nanostructure growth on flexible and thermally sensitive metallic supports.

The growing role of AI in process optimization and adaptive control is increasingly evident. The application of active learning, Bayesian optimization, and PINNs allows for systematic exploration of multidimensional process spaces through predictive feedback. The integration of experimental and simulated data within digital twin architectures supports real-time decision-making and closed-loop optimization during synthesis.

Despite these advances, several challenges remain for the broader implementation of AI-guided pulsed synthesis of ZnO nanostructures on thin metal shells. The most critical issues involve the scarcity and heterogeneity of high-quality experimental datasets, limited interoperability between different diagnostic and data acquisition systems, and the difficulty of maintaining in situ synchronization between AI feedback and rapidly evolving pulsed plasma or laser environments. Furthermore, discrepancies between simulated and experimental outcomes, often referred to as the “sim-to-real” gap, still limit the generalization capability of current AI models. Addressing these challenges will require standardized data protocols, improved uncertainty quantification, and the development of hybrid physics–machine learning frameworks capable of handling dynamic, nonstationary synthesis regimes. Recognizing these limitations is essential for guiding future research toward reproducible, autonomous, and physically interpretable AI-assisted synthesis.

More broadly, the convergence of pulsed plasma and laser technologies with AI-driven modeling will define a new research direction: intelligent pulsed synthesis. This integrated approach enhances reproducibility, scalability, and energy efficiency in the fabrication of ZnO-based nanostructures. The resulting materials demonstrate high potential for applications in gas and biosensing, photocatalysis, optoelectronics, and flexible electronic devices. Future efforts are expected to extend these strategies to other functional oxides and semiconductors, supported by advances in AI–physics coupled modeling and autonomous experimental platforms.